End-to-End Differentiable Predictive Control with Guaranteed Constraint Satisfaction and feasibility for Building Demand Response
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866910066147328000 |
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| author | Xu, Kaipeng Zhi, Zhuo Zhao, Ruixuan Jiang, Keyue |
| author_facet | Xu, Kaipeng Zhi, Zhuo Zhao, Ruixuan Jiang, Keyue |
| contents | The high energy consumption of buildings presents a critical need for advanced control strategies like Demand Response (DR). Differentiable Predictive Control (DPC) has emerged as a promising method for learning explicit control policies, yet conventional DPC frameworks are hindered by three key limitations: the use of simplistic dynamics models with limited expressiveness, a decoupled training paradigm that fails to optimize for closed-loop performance, and a lack of practical safety guarantees under realistic assumptions. To address these shortcomings, this paper proposes a novel End-to-End Differentiable Predictive Control (E2E-DPC) framework. Our approach utilizes an Encoder-Only Transformer to model the complex system dynamics and employs a unified, performance-oriented loss to jointly train the model and the control policy. Crucially, we introduce an online tube-based constraint tightening method that provides theoretical guarantees for recursive feasibility and constraint satisfaction without requiring complex offline computation of terminal sets. The framework is validated in a high-fidelity EnergyPlus simulation, controlling a multi-zone building for a DR task. The results demonstrate that the proposed method with guarantees achieves near-perfect constraint satisfaction - a reduction of over 99% in violations compared to the baseline - at the cost of only a minor increase in electricity expenditure. This work provides a deployable, performance-driven control solution for building energy management and establishes a new pathway for developing verifiable learning-based control systems under milder assumptions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_22104 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | End-to-End Differentiable Predictive Control with Guaranteed Constraint Satisfaction and feasibility for Building Demand Response Xu, Kaipeng Zhi, Zhuo Zhao, Ruixuan Jiang, Keyue Systems and Control The high energy consumption of buildings presents a critical need for advanced control strategies like Demand Response (DR). Differentiable Predictive Control (DPC) has emerged as a promising method for learning explicit control policies, yet conventional DPC frameworks are hindered by three key limitations: the use of simplistic dynamics models with limited expressiveness, a decoupled training paradigm that fails to optimize for closed-loop performance, and a lack of practical safety guarantees under realistic assumptions. To address these shortcomings, this paper proposes a novel End-to-End Differentiable Predictive Control (E2E-DPC) framework. Our approach utilizes an Encoder-Only Transformer to model the complex system dynamics and employs a unified, performance-oriented loss to jointly train the model and the control policy. Crucially, we introduce an online tube-based constraint tightening method that provides theoretical guarantees for recursive feasibility and constraint satisfaction without requiring complex offline computation of terminal sets. The framework is validated in a high-fidelity EnergyPlus simulation, controlling a multi-zone building for a DR task. The results demonstrate that the proposed method with guarantees achieves near-perfect constraint satisfaction - a reduction of over 99% in violations compared to the baseline - at the cost of only a minor increase in electricity expenditure. This work provides a deployable, performance-driven control solution for building energy management and establishes a new pathway for developing verifiable learning-based control systems under milder assumptions. |
| title | End-to-End Differentiable Predictive Control with Guaranteed Constraint Satisfaction and feasibility for Building Demand Response |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2603.22104 |